You were never the bottleneck
Every organisation has a finite amount of IT resource, and it's allocated by priority. If the tool you need isn't business-critical, it doesn't make the cut — no matter how much easier it would make your life. So you work around it. You build spreadsheets that shouldn't be spreadsheets. You do manually what software could do in seconds. You wait.
AI development tools have changed what's possible here. Yes, they're making developers faster — but they're also making the developer optional. If you understand the problem well enough to spec it, you can now build the solution yourself. No queue. No competing priorities. No compromise.
One day. Your problem. A working solution.
This is a full-day, in-person workshop for up to 10 people. You arrive with a real problem — something you'd normally hand to a developer and wait days or weeks for. By the end of the day, you've built it yourself.
The morning covers the fundamentals: how software actually gets made, why each step in the process exists, and which steps you handle versus which your AI tools handle. This isn't a coding lesson — it's a new mental model for how technical work gets done.
The afternoon is a supervised build session. You work on your own project with expert guidance on hand. You leave with something that works, the understanding to keep improving it, and 30 days of email support for when you get stuck.
Is this for you?
This workshop is designed for people who:
- Have deep expertise in their field but no software engineering background
- Know what they want built — a tool, a pipeline, an analytical workflow — and can describe it clearly
- Want to build it themselves rather than outsource it
- Are comfortable with technical thinking, even if they've never written code
If you've ever automated something in your head that nobody's had time to code — you're ready for this.
Three participants, one project
Software projects have defined roles: product managers, architects, developers, testers. Those roles exist for a reason — each one protects the quality of the final product in a different way. In AI-assisted development, the jobs still need to get done. What changes is who does them.
You — the architect and quality authority. You own the problem definition, the domain logic, and the final judgement on whether what's been built actually works. This is the role your expertise has already prepared you for.
Your AI research partner — the design consultant. Handles architecture discussions, explains unfamiliar concepts, helps you think through trade-offs, and turns your intent into a structured plan.
Your AI coding agent — the developer. Writes the code, runs the tests, debugs the errors, and refactors when needed. Fast, tireless, and surprisingly good — but only as good as the direction it receives.
The workshop teaches you to be effective in the first role and to get the most out of the other two.
What the day looks like
Block 1 — Morning: How software gets made (~2 hours)
The full software lifecycle — from requirements through to maintenance — and why each step matters. Where AI tools excel, where they need supervision, and where your domain expertise is irreplaceable. No prior coding experience assumed.
Block 2 — Mid-morning: Scope and setup (~1 hour)
Refine your project into something achievable in an afternoon. Environment check — troubleshoot any setup issues. (A setup guide is sent in advance so you arrive with tools installed.)
Block 3 — Afternoon: Build (~3 hours)
The main event. You work on your project with expert support circulating. Real problems, real progress, real learning by doing.
Block 4 — Late afternoon: Review and next steps (~30 minutes)
Show what you've built. Discuss what's next. Leave with a clear path to keep building on your own.
What you need
- A laptop (Mac, Windows, or Linux)
- A project idea — something you'd normally ask a developer to build in 2-3 days
- Tools installed in advance (we'll send you a step-by-step guide when you register)
About the facilitator
Rhiannon Mulherin is the founder of Praxis Insight, a strategy and analytics consultancy working across CleanTech, MedTech, and FinTech. She has a PhD in physics and 20 years of experience in senior strategy roles, most recently at Shell.
She is not a software engineer. Over the course of a few months (including learning curve) she used AI development tools to build a production-grade quantitative analytics system capable of processing over a decade of tick-level financial data, with a multi-repo architecture, automated testing, and a pluggable analytics model framework. She built it out of frustration: advising a resource-constrained client on what they needed wasn't much use when they didn't have the capacity to build it. So she built it herself.
She teaches this workshop because that experience opened her eyes to a world that had previously been just out of reach. The biggest barrier for most technical professionals isn't ability — it's knowing that this is now possible, and having someone show you how the process works.